Awesome

LADCF - No 1 Algorithm on the public dataset of VOT2018

Codes of 'Learning Adaptive Discriminative Correlation Filters (LADCF) via Temporal Consistency preserving Spatial Feature Selection for Robust Visual Tracking' for VOT2018

Download the Paper

@article{xu2018learning, title={Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking}, author={Xu, Tianyang and Feng, Zhen-Hua and Wu, Xiao-Jun and Kittler, Josef}, journal={arXiv preprint arXiv:1807.11348}, year={2018}}

The tracker codes for the original paper can be download here.

Instruction for LADCF Tracker for VOT2018:

Learning Adaptive Discriminative Correlation Filter on Low-dimensional Manifold (LADCF) utilises adaptive spatial regularizer to train low-dimensional discriminative correlation filters. We follow a single-frame learning and updating strategy: the filters are learned after tracking stage and then updated using a fixed rate [1]. We use HOG [2], CN [3], and ResNet-50 [4] as our features. For deep features, we augment the training data using blur (2 gaussian filters), rotation (-30, -20, -10, 10, 20, 30) and flip (horizontal) [5]. Code modules refer to ECO [6] in feature extraction.

Installation:

Run install.m file to compile the libraries. Copy the tracker_LADCF.m to the vot-workspace. (replace #LOCATION with the path of this folder)

Dependencies:

Operating system:

Ubuntu 14.04 LTS, Matlab R2016a, CPU Intel(R) Xeon(R) E5-2643

References:

[1] Henriques, João F., et al. "High-speed tracking with kernelized correlation filters." IEEE Transactions on Pattern Analysis and Machine Intelligence 37.3 (2015): 583-596.
[2] Dalal, Navneet, and Bill Triggs. "Histograms of oriented gradients for human detection." Computer Vision and Pattern Recognition, 2005. CVPR 2005.
[3] Van De Weijer, Joost, et al. "Learning color names for real-world applications." IEEE Transactions on Image Processing 18.7 (2009): 1512-1523.
[4] He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
[5] Bhat, Goutam, Joakim Johnander, Martin Danelljan, Fahad Shahbaz Khan, and Michael Felsberg. "Unveiling the Power of Deep Tracking." arXiv preprint arXiv:1804.06833 (2018).
[6] Danelljan, Martin, et al. "Eco: Efficient convolution operators for tracking." Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.